Abstract
Analyzing model variability represents a rapidly evolving discipline with increasing applications in different fields. Several efforts have addressed the analysis of a particular variability model represented, for instance, as feature models (FM). However, due to the proliferation of interrelated models, a major challenge today is detecting inter-model inconsistencies; that is, analyzing inconsistencies among inter-related variability models. In this paper, we introduce a proposal for verifying multiple variability models by using scope scenarios. Our approach is based on the SeVaTax method for building variability through functional datasheets, which are inputs to the process. Preliminary evaluation shows promissory results in terms of detected inconsistencies; however performance rises as a challenging issue for spreading the findings.
This work is partially supported by the UNComa project 04/F009 “Reuso de Software orientado a Dominios - Parte II” part of the program “Desarrollo de Software Basado en Reuso - Parte II”.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
This translation of the datasheets to the JSON file is still manual.
References
Acher, M., Collet, P., Lahire, P., France, R.: Familiar: a domain-specific language for large scale management of feature models. Sci. Comput. Program. 78(6), 657–681 (2013). https://doi.org/10.1016/j.scico.2012.12.004
Bak, K., Diskin, Z., Antkiewicz, M., Czarnecki, K., Wkasowski, A.: Clafer: unifying class and feature modeling. Softw. Syst. Model. 15(3), 811–845 (2016). https://doi.org/10.1007/s10270-014-0441-1
Benavides, D., Segura, S., Ruiz-Cortés, A.: Automated analysis of feature models 20 years later: a literature review. Inf. Syst. 35(6), 615–636 (2010). https://doi.org/10.1016/j.is.2010.01.001
Brisaboa, N.R., Cortiñas, A., Luaces, M.R., Pol’la, M.: A reusable software architecture for geographic information systems based on software product line engineering. In: Bellatreche, L., Manolopoulos, Y. (eds.) MEDI 2015. LNCS, vol. 9344, pp. 320–331. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23781-7_26
Buccella, A., Cechich, A., Arias, M., Pol’la, M., Doldan, S., Morsan, E.: Towards systematic software reuse of GIS: insights from a case study. Comput. Geosci. 54(0), 9–20 (2013). https://doi.org/10.1016/j.cageo.2012.11.014
Buccella, A., Cechich, A., Pol’la, M., Arias, M., Doldan, S., Morsan, E.: Marine ecology service reuse through taxonomy-oriented SPL development. Comput. Geosci. 73, 108–121 (2014). https://doi.org/10.1016/j.cageo.2014.09.004
Dhungana, D., GrÃnbacher, P., Rabiser, R.: The DOPLER meta-tool for decision-oriented variability modeling: a multiple case study. Autom. Softw. Eng. 18(1), 77–114 (2011). https://doi.org/10.1007/s10515-010-0076-6
Hartmann, H., Trew, T.: Using feature diagrams with context variability to model multiple product lines for software supply chains. In: 2008 12th International Software Product Line Conference, pp. 12–21, September 2008. https://doi.org/10.1109/SPLC.2008.15
Kowal, M., Ananieva, S., Thüm, T.: Explaining anomalies in feature models. SIGPLAN Not. 52(3), 132–143 (2016). https://doi.org/10.1145/3093335.2993248
Lisboa, L.B., Garcia, V.C., Lucrédio, D., Almeida, E.S., Meira, S.R.D.L., Fortes, R.P.M.: A systematic review of domain analysis tools. Inf. Softw. Technol. 52(1), 1–13 (2010). https://doi.org/10.1016/j.infsof.2009.05.001
Mazo, R., Muñoz-Fernández, J., Rincón, L., Salinesi, C., Tamura, G.: VariaMos: an extensible tool for engineering (dynamic) product lines. In: Proceedings of the 19th International Conference on Software Product Line, SPLC 2015, Nashville, TN, USA, 20–24 July 2015, pp. 374–379 (2015). https://doi.org/10.1145/2791060.2791103
Mendonca, M., Branco, M., Cowan, D.: SPLOT: software product lines online tools. In: Proceedings of the 24th ACM SIGPLAN Conference Companion on Object Oriented Programming Systems Languages and Applications, OOPSLA 2009, pp. 761–762. ACM, New York (2009). https://doi.org/10.1145/1639950.1640002
Metzger, A., Pohl, K., Heymans, P., Schobbens, P., Saval, G.: Disambiguating the documentation of variability in software product lines: a separation of concerns, formalization and automated analysis. In: 15th IEEE International Requirements Engineering Conference (RE 2007), pp. 243–253, October 2007. https://doi.org/10.1109/RE.2007.61
Pohl, K., Böckle, G., van Der Linden, F.J.: Software Product Line Engineering: Foundations. Principles and Techniques. Springer, New York (2005)
Pol’la, M., Buccella, A., Cechich, A.: Automated analysis of variability models: the SeVaTax process. In: Gervasi, O., et al. (eds.) ICCSA 2018. LNCS, vol. 10963, pp. 365–381. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95171-3_29
Rincón, L., Giraldo, G., Mazo, R., Salinesi, C.: An ontological rule-based approach for analyzing dead and false optional features in feature models. Electron. Notes Theor. Comput. Sci. 302, 111–132 (2014). https://doi.org/10.1016/j.entcs.2014.01.023
Roos-Frantz, F., Galindo, J., Benavides, D., Cortés, A.R.: FaMa-OVM: a tool for the automated analysis of OVMs. In: Proceedings of the 16th International Software Product Line Conference, vol. 2, pp. 250–254. ACM (2012)
Rosenmüller, M., Siegmund, N., Thüm, T., Saake, G.: Multi-dimensional variability modeling. In: Proceedings of the 5th Workshop on Variability Modeling of Software-Intensive Systems, VaMoS 2011, pp. 11–20. ACM, New York (2011). https://doi.org/10.1145/1944892.1944894
Segura, S., Benavides, D., Cortés, A.R.: Functional testing of feature model analysis tools. A first step. In: 12th International Conference Software Product Lines, Proceedings, Second Volume (Workshops), SPLC 2008, Limerick, Ireland, 8–12 September 2008, p. 179 (2008)
Sree-Kumar, A., Planas, E., Clariso, R.: Analysis of feature models using alloy: a survey. In: Proceedings 7th International Workshop on Formal Methods and Analysis in Software Product Line Engineering, FMSPLE@ETAPS 2016, Eindhoven, The Netherlands, 3 April 2016, pp. 46–60 (2016). https://doi.org/10.4204/EPTCS.206.5
Wohlin, C., Runeson, P., Hst, M., Ohlsson, M., Regnell, B., Wessln, A.: Experimentation in Software Engineering. Springer, Heidelberg (2012)
Zaid, L., Kleinermann, F., Troyer, O.D.: Applying semantic web technology to feature modeling. In: Proceedings of the 2009 ACM Symposium on Applied Computing, SAC 2009, pp. 1252–1256. ACM, New York (2009). https://doi.org/10.1145/1529282.1529563
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Pol’la, M., Buccella, A., Cechich, A. (2019). Using Scope Scenarios to Verify Multiple Variability Models. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11623. Springer, Cham. https://doi.org/10.1007/978-3-030-24308-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-24308-1_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24307-4
Online ISBN: 978-3-030-24308-1
eBook Packages: Computer ScienceComputer Science (R0)